No abstract

PurposeFerroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.MethodsFerroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.ResultsWe identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.ConclusionOur novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

PurposeThe purpose of this study was to construct a gene signature comprising genes related to both inflammation and pyroptosis (GRIPs) to predict the prognosis of patients with cutaneous melanoma patients and the efficacy of immunotherapy, chemotherapy, and targeted therapy in these patients.MethodsGene expression profiles were collected from The Cancer Genome Atlas. Weighted gene co-expression network analysis was performed to identify GRIPs. Univariable Cox regression and Lasso regression further selected key prognostic genes. Multivariable Cox regression was used to construct a risk score, which stratified patients into high- and low-risk groups. Areas under the ROC curves (AUCs) were calculated, and Kaplan-Meier analyses were performed for the two groups, following validation in an external cohort from Gene Expression Omnibus (GEO). A nomogram including the GRIP signature and clinicopathological characteristics was developed for clinical use. Gene set enrichment analysis illustrated differentially enriched pathways. Differences in the tumor microenvironment (TME) between the two groups were assessed. The efficacies of immune checkpoint inhibitors (ICIs), chemotherapeutic agents, and targeted agents were predicted for both groups. Immunohistochemical analyses of the GRIPs between the normal and CM tissues were performed using the Human Protein Atlas data. The qRT-PCR experiments validated the expression of genes in CM cell lines, Hacat, and PIG1 cell lines.ResultsA total of 185 GRIPs were identified. A novel gene signature comprising eight GRIPs (TLR1, CCL8, EMP3, IFNGR2, CCL25, IL15, RTP4, and NLRP6) was constructed. The signature had AUCs of 0.714 and 0.659 for predicting 3-year overall survival (OS) in the TCGA entire and GEO validation cohorts, respectively. Kaplan-Meier analyses revealed that the high-risk group had a poorer prognosis. Multivariable Cox regression showed that the GRIP signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The nomogram showed good accuracy and reliability in predicting 3-year OS (AUC = 0.810). GSEA and TME analyses showed that the high-risk group had lower levels of pyroptosis, inflammation, and immune response, such as lower levels of CD8+ T-cell infiltration, CD4+ memory-activated T-cell infiltration, and ICI. In addition, low-risk patients whose disease expressed PD-1 or CTLA-4 were likely to respond better to ICIs, and several chemotherapeutic and targeted agents. Immunohistochemical analysis confirmed the distinct expression of five out of the eight GRIPs between normal and CM tissues.ConclusionOur novel 8-GRIP signature can accurately predict the prognosis of patients with CM and the efficacies of multiple anticancer therapies. These GRIPs might be potential prognostic biomarkers and therapeutic targets for CM.

BackgroundBrain metastases (BM) from non-small cell lung cancer (NSCLC) are the most frequent intracranial tumors. To identify patients who might benefit from intracranial surgery, we compared the six existing prognostic indexes(PIs) and built a nomogram to predict the survival for NSCLC with BM before they intended to receive total intracranial resection in China.MethodsFirst, clinical data of NSCLC presenting with BM were retrospectively reviewed. All of the patients had received total intracranial resection and were randomly distributed to developing cohort and validation cohort by 2:1. Second, we stratified the cohort using a recursive partitioning analysis(RPA), a score index for radiosurgery (SIR), a basic score for BM (BS-BM), a Golden Grading System (GGS), a disease-specific graded prognostic assessment (DS-GPA) and by NSCLC-RADES. The predictive power of the six PIs was assessed using the Kaplan–Meier method and the log-rank test. Third, univariate and multivariate analysis were explored, and the nomogram predicting survival of BMs from NSCLC was constructed using R 3.2.3 software. The concordance index (C-index) was calculated to evaluate the discriminatory power of the nomogram in the developing cohort and validation cohort.ResultsBS-BM could better predict survival of patients before intracranial surgery compared with other PIs. In the final multivariate analysis, KPS at diagnosis of BM, metachronous or synchronous BM and the histology of lung cancer appeared to be the independent prognostic predictors for survival. The C-index in the developing cohort and validation cohort were 0.75 and 0.71 respectively, which was better than the C-index of the other six PIs.ConclusionsThe new nomogram is a promising tool in further choosing the candidates for intracranial surgery among NSCLC with BM and in helping physicians tailor suitable treatment options before operation in clinical practice.

A cationic monofunctional platinum anticancer drug, phenanthriplatin (PhenPt(II)), exhibits promising anticancer effect on various cancer cell lines. Unlike the conventional platinum(II) drugs, PhenPt(II) is more likely to bind the N7 adenosine base of DNA in situ, and consequently resulting in a unique cellular response profile and unusual potency. However, since this drug is positively charged, it can easily bind to plasma protein that leads to rapid systematic clearance and deleterious toxicities, which greatly limits its in vivo application. Herein, a lipophilic phenanthriplatin (PhenPt(IV)) prodrug is synthesized. To further reduce its toxicity, a negatively charged polymer P1 with reduction responsiveness is assembled with PhenPt(IV) to form PhenPt(IV) NPs. In comparison to cisplatin, PhenPt(IV) NPs exhibit up to 30 times greater in vitro potency against various cancer cell lines. Additionally, in vivo, no obvious side effect is found on PhenPt(IV) NPs. Significant enhancement in tumor accumulation and improvement of drug efficacy in 4T1 tumor model are demonstrated. Taken together, this study provides a promising strategy for the clinical translation of phenanthriplatin.